This disclosure includes systems and techniques that verify an educational quality of online videos, employing a platform that enables experts to endorse and/or recommend videos that are more readily accessible to end users. Certain aspects include a computing device for a service provider that receives a query from a user for video content. The computing device forwards the query to a hosting service, and in response to the query, the computing device receives, from the hosting service, a list of video content items it hosts. The computing device identifies, for each video content item within the list of video content items, an endorsement value associated with one or more experts. In some aspects, the computing device generates an aggregated list of video content items from a plurality of lists of video content items based on endorsement values. The computing device provides recommendations to the user using the endorsement values.

Background
This study focuses on health-related content (HRC) on YouTube and addresses the issue of misinformation on this platform. While previous research centered on content evaluations by experts, this study takes a user-centered approach and aims to explore users’ experiences with and perceptions of HRC videos and to establish links between these perceptions and some socio-demographic characteristics including age, gender, profession, and educational level.
Methods
A quantitative research design was used in the study. 3,000 YouTube users responded to a 35-item anonymous questionnaire to collect information about the content they watch toward decision-making, their perceptions of the usefulness and bias of this content, what they identify as quality indicators for HRC, and what they recommend to improve the quality of such content on YouTube. The data were analyzed using descriptive statistics, frequency, and correlation analyses.
Results
The results reveal that 87.6 percent (n=2630) of the participants watch HRC on YouTube, and 84.7 percent (n=2542) make decisions based on what they watch. Exercise and bodybuilding videos are the most popular, with over half of the participants watching them. 40 percent of the users watch YouTube videos to decide whether to consult a doctor or adopt specific health-related practices. In contrast to evaluations by experts in previous studies, most respondents perceive HRC videos on YouTube as useful and do not find connections between video quality and surface features like the number of views and likes. Weak or no correlations were observed between the perceived usefulness of HRC videos and age, gender, profession, or educational level. Participants’ recommendations for enhancing HRC quality align with previous research findings.
Conclusions
Users turn to YouTube not only for health information but also as a decision-making tool. Combined with their generally positive attitudes towards content quality on this platform, this can have significant consequences for their health. Follow-up studies are needed to get more insights into decision-making behaviors and how users assess their decisions in retrospect.

Background
YouTube is a valuable source of health-related educational material which can have a profound impact on people’s behaviors and decisions. However, YouTube contains a wide variety of unverified content that may promote unhealthy behaviors and activities. We aim in this systematic review to provide insight into the published literature concerning the quality of health information and educational videos found on YouTube.
Methods
We searched Google Scholar, Medline (through PubMed), EMBASE, Scopus, Direct Science, Web of Science, and ProQuest databases to find all papers on the analysis of medical and health-related content published in English up to August 2020. Based on eligibility criteria, 202 papers were included in our study. We reviewed every article and extracted relevant data such as the number of videos and assessors, the number and type of quality categories, and the recommendations made by the authors. The extracted data from the papers were aggregated using different methods to compile the results.
Results
The total number of videos assessed in the selected articles is 22,300 (median = 94, interquartile range = 50.5–133). The videos were evaluated by one or multiple assessors (median = 2, interquartile range = 1–3). The video quality was assessed by scoring, categorization, or based on creators’ bias. Researchers commonly employed scoring systems that are either standardized (e.g., GQS, DISCERN, and JAMA) or based upon the guidelines and recommendations of professional associations. Results from the aggregation of scoring or categorization data indicate that health-related content on YouTube is of average to below-average quality. The compiled results from bias-based classification show that only 32% of the videos appear neutral toward the health content. Furthermore, the majority of the studies confirmed either negative or no correlation between the quality and popularity of the assessed videos.
Conclusions
YouTube is not a reliable source of medical and health-related information. YouTube’s popularity-driven metrics such as the number of views and likes should not be considered quality indicators. YouTube should improve its ranking and recommender system to promote higher-quality content. One way is to consider expert reviews of medical and health-related videos and to include their assessment data in the ranking algorithm.

As major product of information technology, YouTube is a ubiquitous source for education, also in the field of information technology. Learners, however, are facing the increasing problem of finding appropriate videos on YouTube efficiently. Users' rating in terms of Likes and Dislikes could provide a starting point. However, it is unclear what the number of Likes and Dislikes reveal about the video. This paper tries to create links between different video features and users' rating of YouTube's educational content. For this purpose, 300 educational videos were collected and analyzed and regression models were established that describe the number of Likes per view and the number of Dislikes per view as functions of different video features and production styles. Results show that the number of Likes per view can be predicted more reliably than the number of Dislikes per view. The number of Likes per view increases with higher video resolution and higher talking rate (words per second), and when the instructor or tutor speaks English as a native language. Videos using explanations on paper or whiteboard as well as videos that use more than one style attract more Likes per view. In contrast, the model that describes the number of Dislikes per view has a low adjusted R-squared and the contribution of its significant variables is rather difficult to interpret. This suggests that further research is required to understand users' behavior in terms of disliking an educational video.

YouTube provides a vital source for self-directed learning. YouTube’s search engine, however, ranks videos according to popularity, relevancy, and view history rather than quality. The effect of this ranking on learners’ behavior and experience is not clear: Do learners tend to choose from the top of the returned search list? Does the choosing behavior affect their learning? Is the type of sought knowledge relevant in this process? To answer these questions, we conducted two experiments with sophomore-level students in electrical and computer engineering programs. The students were asked to learn about two new topics by watching YouTube videos of their choice. The first topic conveys procedural knowledge about using the Quine McCluskey algorithm for minimizing logical functions. The second topic relates to the concept of the set-reset latch. In each learning session, the students had to report their watching behavior and experience by responding to an online questionnaire as well as to solve a problem related to the respective topic. The results show a clear tendency to choose from the top of the returned list. However, students’ performance in problem-solving was found to be uncorrelated with the choosing behavior. These results were similar for procedural and conceptual learning although the students’ performance in solving the conceptual problem was significantly lower. These findings indicate that university students who seek YouTube for self-directed learning can freely choose from the top of the returned search list without concern. There is no evident harm in doing so. However, students need to be thoughtful when using YouTube for conceptual learning. They should use different strategies such as watching multiple videos, selecting videos with higher viewer ratings, or watching videos with related procedural knowledge to support the learning of new concepts.

YouTube has evolved to a global platform for formal and informal education. In contrast to traditional sources of learning multimedia, YouTube is a social platform with numerous characteristics that make its real value for education not obvious. We neither know how reliable the learning content on YouTube is, what best-practice strategies for using this platform in education are nor how watching YouTube affects students’ performance and behavior. To shed light on these questions, we conducted a scoping review of the literature on YouTube and education. A total of 647 publications were included and analyzed thematically. Four research themes could be identified: (1) Content creation and assessment (2) User attitudes and acceptance (3) Usage strategies and behaviors (4) Impact on student learning. The findings of the respective studies were analyzed and compiled theme by theme. The main results of this review are: (1) There is an increasing concern about content quality on YouTube. (2) Despite versatile production and usage strategies, no relationships were established between such strategies and learning. (3) Most studies on the impact of YouTube on student learning reported positive results in terms of enhanced skills, competencies, interest, motivation, engagement levels, or test performance. We conclude that YouTube is a rich, free, easy-to-use, and enjoyable source of learning content. However, the challenges and risks associated with this platform suggest that it is best suitable for guided learning where teachers make or select the content and include it in a well-defined, pedagogy-driven learning context.

As major product of information technology, YouTube is a ubiquitous source for education, also in the field of information technology. Learners, however, are facing the increasing problem of finding appropriate videos on YouTube efficiently. Users' rating in terms of Likes and Dislikes could provide a starting point. However, it is unclear what the number of Likes and Dislikes reveal about the video. This paper tries to create links between different video features and users' rating of YouTube's educational content. For this purpose, 300 educational videos were collected and analyzed and regression models were established that describe the number of Likes per view and the number of Dislikes per view as functions of different video features and production styles. Results show that the number of Likes per view can be predicted more reliably than the number of Dislikes per view. The number of Likes per view increases with higher video resolution and higher talking rate (words per second), and when the instructor or tutor speaks English as a native language. Videos using explanations on paper or whiteboard as well as videos that use more than one style attract more Likes per view. In contrast, the model that describes the number of Dislikes per view has a low adjusted R-squared and the contribution of its significant variables is rather difficult to interpret. This suggests that further research is required to understand users' behavior in terms of disliking an educational video.

Viewers' rating of educational online videos provides a clue about content quality and is frequently used as filter in video search and recommendation systems. However, it is unclear what motivates students to give a positive or negative rate to an educational video. In the presented study we tried to find out the reasons behind students' ratings. A total of 51 students were asked to rate a total of 54 YouTube's videos and to write using their own words the reasons for each rating. After cleaning the responses, 1602 and 732 reasoning statements for Like or Dislike ratings were analyzed using group concept mapping, respectively. A sentimental framework could be established consisting of six clusters in this order of significance: explanation, technical presentation, content, voice and language, efficiency, and interestingness. Toward generalizing this framework, we investigated the relationship between the online ratings of the selected videos and the active ratings by our students. We found out that the online liking ratio strongly relates to the liking ratio by our students (R2 = 91.3%). Furthermore, we investigated students' rating tendencies as function of their performance and gender. We found out that higher-performance students are more likely to like or dislike a video, whereas lower-performance students are less determined. The gender only affected the disliking tendency with female students making more use of the corresponding rate. The findings of the study can help students searching for educational videos as well as producers of such videos towards improved content quality and learning outcomes.

YouTube has become a global platform for learning and teaching. Its design as a social medium, its rapidly growing content and the obscurity of its search and recommendation system, however, frequently leave users with suboptimal results. Trying to give guidance, many professional websites started to publish ranked lists of educational channels. These lists, however, are highly non-conjoint and different in length, which challenges their general usefulness. This study first highlights some aspects and issues related to ranking YouTube’s educational channels by a qualitative and quantitative analysis of 193 lists collected from 101 websites. Then, an iterative multi-algorithm approach is proposed to derive aggregated ranked lists starting from these online lists for three categories: general education, science and history. The aggregated lists were then correlated with surface features of the channels including the channel’s lifetime and the total number of videos, views and subscribers. Also, an alternative rating-based ranking was established by analysing a total of 2900 videos from the different channels. The results show that the aggregated ranked list of science channels has strong correlation with surface channel features. In contrast, the aggregated ranks of history channels are more correlated with viewers’ positive ratings. The aggregated ranks of general education channels neither relate to channel features nor to viewers’ ratings. Based on these findings several remarks and recommendations for the generation, usage, and research on ranked lists and rank aggregation of YouTube’s educational channels are given.